Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer
Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer
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DOI：
10.1073/pnas.1013699108
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发表时间：
2011-02-22
影响因子：
11.1
通讯作者：
Krek, Wilhelm
中科院分区：
文献类型：
--
作者：
Cima, Igor;Schiess, Ralph;Krek, Wilhelm
实现癌症个性化医疗的一个关键障碍是生物标志物的识别。在这里，我们描述了一个两阶段策略，用于发现与特定致癌突变相对应的血清生物标志物特征，并将其应用于前列腺癌（PCa）中常见的磷酸酶和紧张素同源物（PTEN）肿瘤抑制基因失活的背景下。在我们方法的第一阶段，我们从野生型和pten缺失小鼠的血清和前列腺组织中鉴定出775种n-连接糖蛋白。使用无标记定量蛋白质组学，我们发现Pten失活导致小鼠前列腺和血清糖蛋白组的可测量扰动。在生物信息学优先级之后，在第二阶段，我们应用靶向蛋白质组学检测和量化了PCa患者和对照个体血清中的39种人类同源候选生物标志物。通过机器学习分析所得的蛋白质组学图谱，建立组织PTEN状态和PCa诊断分级的预测回归模型。我们的方法提出了一条合理的癌症生物标志物发现和初步验证的一般途径，以癌症遗传学为指导，基于实验小鼠模型、基于蛋白质组学的技术和计算建模的整合。
A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog ( PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.